Aperag
🐍 ☁️ 🏠 - Production-ready RAG platform combining Graph RAG, vector search, and full-text search. Best choice for building your own Knowledge Graph and for Context Engineering
What is Aperag?
Aperag is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to 🐍 ☁️ 🏠 - production-ready rag platform combining graph rag, vector search, and full-text search. best choice for building your own knowledge graph and for context engineering
🐍 ☁️ 🏠 - Production-ready RAG platform combining Graph RAG, vector search, and full-text search. Best choice for building your own Knowledge Graph and for Context Engineering
This server falls under the Knowledge & Memory category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- 🐍 ☁️ 🏠 - Production-ready RAG platform combining Graph RAG,
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx aperagConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use Aperag
ApeRAG is a production-ready Retrieval-Augmented Generation platform that combines Graph RAG, vector search, and full-text search into a single self-hosted service, exposing its knowledge-retrieval capabilities through a built-in MCP server. It lets AI assistants browse document collections, perform hybrid searches across large corpora, and answer natural-language questions grounded in your own documents. Teams use ApeRAG to build custom knowledge graphs over internal documentation, codebases, or research papers, making it a strong foundation for context engineering in enterprise AI workflows.
Prerequisites
- Docker and Docker Compose installed (minimum 2 CPU cores, 4 GB RAM)
- An ApeRAG API key generated from the web interface after deployment
- An MCP-capable client such as Claude Desktop or any MCP-compatible AI assistant
- Git installed to clone the repository
Clone the repository and copy the environment template
Clone the ApeRAG repository and create your .env file from the provided template. Review the template to configure any LLM provider keys or storage settings before starting the stack.
git clone https://github.com/apecloud/ApeRAG.git
cd ApeRAG
cp envs/env.template .envStart the ApeRAG stack with Docker Compose
Launch all services using Docker Compose. The stack pulls the latest images automatically. Use the optional docray profile if you need enhanced document parsing with OCR capabilities.
# Standard startup
docker-compose up -d --pull always
# With enhanced document parsing (optional)
DOCRAY_HOST=http://aperag-docray:8639 docker compose --profile docray up -dOpen the web interface and create a collection
Navigate to http://localhost:3000/web/ to access the ApeRAG dashboard. Create a knowledge collection, upload your documents, and wait for the indexing pipeline to complete. Then generate an API key from the settings page.
Configure your MCP client to connect to ApeRAG
Add the ApeRAG MCP server to your client configuration. The server runs at http://localhost:8000/mcp/ and requires your API key as a Bearer token in the Authorization header.
{
"mcpServers": {
"aperag-mcp": {
"url": "http://localhost:8000/mcp/",
"headers": {
"Authorization": "Bearer your-api-key-here"
}
}
}
}Verify the connection and explore your knowledge base
Ask your AI assistant to list available collections or perform a search. The MCP server exposes three primary tools: collection browsing, hybrid search (vector + full-text + graph), and natural language question answering over documents.
Aperag Examples
Client configuration
MCP client config for connecting Claude Desktop to a locally running ApeRAG instance.
{
"mcpServers": {
"aperag-mcp": {
"url": "http://localhost:8000/mcp/",
"headers": {
"Authorization": "Bearer your-aperag-api-key"
}
}
}
}Prompts to try
Example prompts that exercise ApeRAG's collection browsing, hybrid search, and document Q&A capabilities.
- "List all available knowledge collections in ApeRAG"
- "Search the documentation collection for information about authentication middleware"
- "What does the onboarding guide say about setting up a new developer environment?"
- "Find all documents related to database schema migrations using hybrid search"
- "Ask the knowledge base: what are the main differences between our v1 and v2 API?"Troubleshooting Aperag
MCP connection fails with 401 Unauthorized
Ensure your API key is correct and included as 'Authorization: Bearer <key>' in the headers. API keys are generated per-user in the ApeRAG web interface. You can also set APERAG_API_KEY as an environment variable if the client supports it.
Docker Compose fails to start due to insufficient resources
ApeRAG requires at least 2 CPU cores and 4 GB RAM. Check Docker Desktop resource settings and increase memory allocation. On Linux, verify that Docker has sufficient system resources with 'docker info'.
Documents are uploaded but search returns no results
Indexing takes time after upload, especially for large documents or when using the Graph RAG pipeline. Check the ApeRAG dashboard at http://localhost:3000/web/ for indexing status. The API documentation at http://localhost:8000/docs shows indexing job endpoints.
Frequently Asked Questions about Aperag
What is Aperag?
Aperag is a Model Context Protocol (MCP) server that 🐍 ☁️ 🏠 - production-ready rag platform combining graph rag, vector search, and full-text search. best choice for building your own knowledge graph and for context engineering It connects AI assistants to external tools and data sources through a standardized interface.
How do I install Aperag?
Follow the installation instructions on the Aperag GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with Aperag?
Aperag works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is Aperag free to use?
Yes, Aperag is open source and available under the Apache-2.0 license. You can use it freely in both personal and commercial projects.
Aperag Alternatives — Similar Knowledge & Memory Servers
Looking for alternatives to Aperag? Here are other popular knowledge & memory servers you can use with Claude, Cursor, and VS Code.
MemPalace
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Kratos
★ 25.7k🏛️ Memory System for AI Coding Tools - Never explain your codebase again. MCP server with perfect project isolation, 95.8% context accuracy, and the Four Pillars Framework.
Context Mode
★ 15.4kAn MCP server that preserves LLM context by intercepting large data outputs and returning only concise summaries or relevant sections. It enables efficient sandboxed code execution, file processing, and documentation indexing across multiple programm
Memu
★ 13.7kMemory for 24/7 proactive agents like OpenClaw.
MemOS
★ 9.3kMemOS (Memory Operating System) is a memory management operating system designed for AI applications. Its goal is: to enable your AI system to have long-term memory like a human, not only remembering what users have said but also actively invoking, u
Everos
★ 5.4kBuild, evaluate, and integrate long-term memory for self-evolving agents.
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